Genetic Diversity in Rosa as Revealed by RAPDs

We aim to study the variability within genus Rosa. To accomplish this we have analyzed a plant material collection (109 accessions) including all sections but one, as well as many intermediate forms and hybrids. We also aim to study the consistency of the groups considered within section Caninae (‘caninae’, ‘rubiginosa’ and ‘tomentosa’) as well as of the subgenus Hulthemia. A dendrogram was constructed based on RAPDs data. The variability found in the dendrogram was discussed according to sectional status and geographic origin. Our results indicate that there is no clear distinction between Caninae groups when many intermediate forms are considered. Besides, the subgenus Hulthemia seems to merit just a sectional status as proposed by other authors for other subgenus. The heterogeneity found in the dendrogram with respect to sectional status suggests the lack of clear reproductive barriers as is common with long lived woody perennial plants. Sect. Cassiorhodon may be considered as the Type of the genus since it shows the widest geographical distribution, the widest crossing ability within the Genus and it appears in most groups of the dendrogram suggesting to be the most representative Section

SOC-V-11 New serum miRNA biomarkers to predict liver steatosis by valproic acid in paediatric epileptic patients

Depakine (Valproate, VPA) has been the first line, most-frequently prescribed, anti-epileptic drug in children for the past 50 years. Idiosyncratic hepatotoxicity (iDILI) by VPA has been demonstrated in several case reports, where microvesicular liver steatosis was the most frequent feature. Moreover, more than half of VPA-treated patients could have silent fatty liver as demonstrated by ultrasounds. Extensive experimental studies support that VPA has a high potential to induce steatosis in hepatocytes. However, there is an apparent lack of significant hepatic problems in the Neuropediatric Units, despite transaminitis is not uncommon. One of the reasons could be that iDILI and liver steatosis diagnosis lack specific biomarkers. Thus, it is likely that a relevant number of children under VPA treatment may have a significant, but sub-clinical, hepatosteatosis

Spreadsheet for the simulation of artificial neural networks (ANNs)

La utilización de Redes de Neuronas Artificiales (RNA) en problemas de predicción de series de tiempo, clasificación y reconocimiento de patrones ha aumentado considerablemente en los últimos años. Programas informáticos de matemáticas de propósito general tales como MATLAB, MATHCAD y aplicaciones estadísticas como SPSS y S-PLUS incorporan herramientas que permiten implementar RNAs. A esta oferta de software hay que añadir programas específicos como NeuralWare, EasyNN o Neuron. Desde un punto de vista educativo, el acceso de los estudiantes a estos programas puede ser difícil dado que no están pensadas como herramientas didácticas. Por otro lado, las hojas de cálculo como Excel y Gnumeric incorporan utilidades que permiten implementar RNAs y son de fácil acceso para los estudiantes. El objetivo de este trabajo es proporcionar un pequeño tutorial sobre la utilización de Excel para implementar una RNA que nos permita ajustar los valores de una serie de tiempo correspondiente a actividad cerebral alfa y que permita al alumno entender el funcionamiento de estos dispositivos de cálculo.In recent years, the use of Artificial Neural Networks or ANNs has increased considerably to solve prediction problems in time series, classification and recognition of patterns. General-purpose mathematical programs such as MATLAB, MATHCAD and mathematical and statistical programs such as SPSS and S-PLUS incorporate tools that allow the implementation of ANNs. In addition to these, specific programs such as NeuralWare, EasyNN, or Neuron, complete the software offer using ANNs. From an educational point of view, an aspect that concerns the authors of this work, student access to these programs can be expensive or, in sorne case, unadvisable given the few possibilities they provide as didactic instruments. These programs are usually easy to use but do not facilitate the understanding of the technique used. On the other hand, spreadsheets like Excel or Gnumeric incorporate tools that allow all of the necessary calculations to implement an ANN. These programs are user-friendly to the point that they are used by university laboratories, as well as psychology, economic science, and engineering students, to mention a few. This paper provides a small tutorial on the use of a spreadsheet, specifically Excel, to implement an ANN to adjust the values of a time series corresponding to cerebral alpha activity

I Jornada de Aulas Abiertas: Encuentro de Docentes de la Facultad de Ciencias Económicas

La Jornada de Aulas Abiertas quiere ser una oportunidad para que los docentes de la Facultad de Ciencias Económicas nos encontremos en un espacio de reflexión y revisión de nuestras prácticas, distendido, cálido y respetuoso, que nos permita compartir nuestras experiencias cotidianas en las aulas, tanto presenciales como virtuales. Es la posibilidad de conocernos, intercambiar, aprender y contagiarnos de las inquietudes y el entusiasmo que muchos docentes ponen en juego cotidianamente. En el marco de propuestas de enseñanza, se analizaron recursos multimediales, materiales de estudio, aulas virtuales, redes sociales, aplicaciones web, juegos y actividades de evaluación y coevaluación originales; también se abordaron problemáticas y propuestas para favorecer vinculaciones con la práctica profesional. Estas fueron algunas de las cuestiones abordadas y compartidas en las presentaciones de nuestros colegas. Distintas propuestas, pero siempre con el propósito de favorecer las oportunidades de aprendizaje de nuestros estudiantes. Esta publicación pretende ampliar el alcance de esta actividad. Es una invitación para que los y las docentes que participaron puedan revisar nuevamente aquellas actividades que les parecieron valiosas, o las que no pudieron presenciar. Y para aquellos/as que no tuvieron la posibilidad de estar presentes, puedan descubrir cuánto podemos hacer para que nuestros estudiantes aprendan más y mejor, y se animen a iniciar sus propios recorridos. Esperamos repetir este evento para seguir aprendiendo de las iniciativas de los/las docentes de nuestra Facultad, poder hablar de lo que nos preocupa y nos enorgullece, en particular de las propuestas que desarrollamos en el aula para favorecer la comprensión, promover el entusiasmo, abordar temas complejos y errores frecuentes de nuestros estudiantes. Desde el Área de Formación Docente y Producción Educativa queremos agradecer a las autoridades de nuestra Facultad por acompañarnos en este desafío y a los/las docentes que estuvieron presentes compartiendo sus experiencias.Fil: Sabulsky, Gabriela. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Margaría, Oscar A. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Iturralde, Ivan. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Domenech, Roberto. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Torrico, Julieta. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Estigarribia, Lucrecia. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Gohlke, Guillermo. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Rosenfeld, Valeria. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Montenjano, Franco. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Atienza, Bárbara. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Becerra, Natalia. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Alonso, Micaela. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Tomatis, Karina. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Saunders, Shirley. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: David, María Laura. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Flores, Verónica Andrea. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Heckmann, Gerardo. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Vega, Juan José. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Trucchi, Carlos. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Ferro, Flavia. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Díaz, Cecilia. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Peretto, Claudia. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Racagni, Josefina. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Guardiola, Mariana. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: López, Sonia. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Beltrán, Natacha. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Russo, Paulo. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Sánchez, Pablo. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Rocha Vargas, Marcelo. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Flores, Norma. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Arévalo, Eliana. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Pacheco, Verónica. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Delmonte, Laura. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Stanecka, Nancy. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Caminos, Ana Belén. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Ahumada, María Inés. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Caro, Norma Patricia. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Bravino, Laura. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Giménez, Siria Miriam. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Perona, Eugenia. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Cuttica, Mariela. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: García, Gladys Susana. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Cohen, Natalia. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Tapia, Sebastián. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Erazu, Damián. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Torres, César. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Casini, Rosanna Beatriz. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Rosales, Julio. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Infante, Roberto Adrián. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Ricci, María Beatriz. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Römer, Gabriela. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Goyeneche, Noel. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Marzo, Emanuel. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Olmos, Mariano. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Bottino, Cecilia. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Cacciagiú, Victor. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Scidá, María Florencia. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Guajardo Molina, Vanesa. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Batistella, Silvana del V. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Huanchicay, Silvia. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Jones, Carola. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Cassutti, Marcela Beatriz. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Sánchez, Juan Nicolás. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Arónica, Sandra. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Ortega, Fernando. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Peretti, Florencia. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Tagle, María Mercedes. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Asís, Gloria Susana. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Ortiz Figueroa, Ana María. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Giménez, Miriam Mónica. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Magnano, Cecilia. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Arias, Verónica. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina

Strategies and advances to identify candidate genes controlling low vicine-convicine in faba vean (Vicia faba L.)

Trabajo presentado en el Second International Legume Society Conference "Legumes for a sustainable world" (ILS2), celebrado en Tróia (Portugal) del 11 al 14 de octubre de 2016.Vicine and convicine (v-c), limit the use of faba bean as food and feed. A single gene vc-, responsible for a 10-20 fold reduction in v-c, 10 cM apart from the white hilum has been describ ed (Duc et al. 1989; 2004). Khazaei et al. (2015), reported a QTL for v-c content on chromosome I (Chr I) and although several closer markers were identified (Gutierrez et al. 2006) no candidate genes are so far available. The identification of suitable candidate genes is limited for the lack of knowledge of the pathway for the v-c biosynthesis and the large faba bean genome size (~13 Gbp). In an attempt to determine which enzymes or transcriptional regulat ors could be encoded by the vc-gene, we are applying a combination of genetic linkage and comparative genomic approaches. To facilitate high-throughput genome profiling DarTSeq (Kilian et al. 2012) has been applied in a RIL population from the cross Vf6 (high v-c) x vc-(low v-c) generating more than 10.000 markers. For the assignment of the linkage group to specific chromosomes, 58 EST anchor marker from the reference consensus map (Satovic et al. 2013) were assayed and 14 of them could be mapped. On the other hand, 37 SNPs, from the KASPar assay platform (Semagn et al. 2014) belonging to Chr I, were genotyped and 9 of them, resulted polymorphic. The moderate conservation of the faba bean Chr 1 with the Medicago Chr 2 confined the target region between Medtr2g005900 and Medtr2g026550. To fine mapping the v-c position, primers for 61 new candidates were designed using both the Medicago (29) and the faba bean (32) transcriptome sequences (Ocaña et al. 2015) and 12 genes could be mapped. The forthcoming linkage analysis may provide potential candidate genes for the target trait.N

Identifying positional candidate genes controlling low vicine-convicine in faba bean (Vicia faba L.)

Trabajo presentado en la International Conference Advances in grain legume breeding, cultivation and uses for a more competitive value-chain, celebrada en Novi Sad (Serbia) el 27 y 28 de septiembre de 2017.Vicine and convicine (v-c) are faba bean compounds with anti-nutritional effects in monogastric animals and potential toxicity in humans. A single gene, vc-, responsible for a 10-20 fold reduction (1, 2) was identified and several attempts to identify candidate genes (3, 4) have been pursued. The approach is limited by its reliance on the priori knowledge about the physiological function of candidates. In case of v-c, the lack of knowledge of the v-c synthetic pathway, the large faba bean genome size (~13 Gbp) and the lack of a reference genome, result in a significant bottleneck for the application of this approach. Although the task is challenging, significant progress has been achieved within LEGA TO and several closer markers have been identified. To determine which enzymes or transcriptional factors could be encoded by the vc-gene, we have used comparative genomic approaches (5), KASPar SNP genotyping assays (6) and high-throughput genome profiling DarTSeq (7) in a F2 from the cross Vf6 x vc-, to generated a map with more than 4000 markers. The target region was confined between Medtr2g008210 and Medtr2g010180, containing 136 genes, but the causative gene remains unknown. Recently, 21 new candidates have been genotyped in the population, using the Medicago and the faba bean transcriptome sequences (8). Fourteen should be discarded due to nonspecific/lack of amplification or to the absence of SNPs in the target sequence. The remaining seven could bemapped, thus providing potential candidate genes and offering a step towards breeding lines free of these compoundsN

Up-regulation of resistance gene analogs (RGA) in chickpea in the early response to Fusarium wilt

The aim of the present work was to determinate if resistant gene analogs (RGAs) previously identified and characterized by our group are involved in the early response to Fusarium oxysporum f. sp. ciceris (Foc). The expression profile of RGAs was determined using quantitative real-time polymerase chain-reaction (qPCR) in WR315 (resistant) and ILC3279 (susceptible) genotypes in response to Foc race 5 inoculation. Our results demonstrate that RGA05 and RGA07 were induced after Foc race 5 treatment at 2 days after inoculation (DAI) in the resistant genotype. In contrast RGA10 was induced in both resistant and susceptible plants, although the basal level of this gene was higher in the resistant genotype. On the contrary, no significant changes were observed for any of these genes at 7 DAI. Our results suggest a role of some of the candidate genes in the early response against fusarium wilt, mainly as part of the inducible defensive system. Thus, these genes could be a good start point for further studies such as candidate gene mapping or understand the bases for resistance in chickpea. © 2011 Springer Science+Business Media B.V.This study was partly funded by grant INIA RTA2007-00030 from the Spanish Science and Technology Ministry and FEDER.Peer Reviewe

QTLs for ascochyta blight resistance in faba bean (Vicia faba L.): validation in field and controlled conditions

Ascochyta blight is an important disease of faba bean (Vicia faba L.). Yield losses can be as high as 90% and losses of 35–40% are common. The line 29H is one of the most resistant accessions to the pathogen (Ascochyta fabae Speg.) ever described. In this work, we aimed to validate across generations the main quantitative trait loci (QTLs) for ascochyta blight resistance identified in the cross 29H × Vf136 and to test their stability under field conditions. QTLs located on chromosomes II and III have been consistently identified in the recombinant inbred line (RIL) population of this cross, in both controlled (growth chamber) and field conditions and, thus they are good targets for breeding. In addition, a new QTL for disease severity on pods has been located on chromosome VI, but in this case, further validation is still required. A synteny-based approach was used to compare our results with previous QTL works dealing with this pathogen. Our results suggest that the QTL located on chromosome II, named Af2, is the same one reported by other researchers, although it is likely that the donors of resistance differ in the allele conferring the resistance. By contrast, the location of Af3 on chromosome III does not overlap with the position of Af1 reported by other authors, suggesting that Af3 may be an additional source of resistance to ascochyta blight.Peer reviewe

Validation of quantitative trait loci for height-related traits in faba bean

Trabajo presentado en el Second International Legume Society Conference "Legumes for a sustainable world" (ILS2), celebrado en Tróia (Portugal) del 11 al 14 de octubre de 2016.Most faba bean genotypes show indeterminate growth habit. These genotypes may become very tall when water is available. Indeed faba bean crops may grow to a height of 2 m at maturity although faba bean crops are not usually that tall. Excessive plant height (PH) may result in plant lodging. Besides, the crop may result in poor pod set due to predominant vegetative growth, uneven maturity. Accordingly shorter genotypes are imp ortant in faba bean breeding. Other height-related traits are also important. The height of the lower flower (HLF) and the height of the lower pod (HLP) are important agronomic traits. Pods located near ground may remain unharvested and they may be more af fected by disease incidence. Previous studies in our group identified preliminary quantitative trait loci (QTL) for these traits in the population derived from the cross VF6 x Vf27. In this work we have validated QTL for PH, HLP and HLF at field conditions. Besides, the role of candidate genes for height-related traits in model species was investigated in relation with these QTL.This work was financed by the Ministerio de Innovación y Ciencia (MICINN) grant AGL2008-02305 and FEDER. MDR was recipient of a pre-doctoral FPI fellowship associated to this research grant.N

Identification of plant architecture and yield-related QTL in Vicia faba L.

Low and unstable yields across seasons and environments are among the main reasons which make profitability for farmers too low. Along with resistance/tolerance to biotic and abiotic stresses, plant architecture and yield-related traits are the main determinants of yield stability. The current study was conducted to identify and validate quantitative trait loci (QTL) for plant architecture and yield-related traits in faba bean; our results provide novel information about the genetics of plant architecture traits in this crop. An equina × paucijuga recombinant inbred line population was derived and submitted to field experiments at Córdoba (Spain) over a period of four seasons. Stable QTL were identified for eight of the traits evaluated. QTL clusters were identified on almost each chromosome. The high inter-trait correlations between some of the traits controlled by a cluster of QTL might reflect either a set of closely linked loci or, more likely, pleiotropic effects. The stability of many of these major QTL in different years offers the possibility of exploiting them via marker-assisted selection. Further fine mapping of these target regions will help to identify potential candidate genes using synteny.This work was financed by the Spanish Ministerio de Innovación y Ciencia (MICINN) grant AGL2008-02305 and the Instituto Nacional de Investigación Agraria (INIA) grant RTA2013-00025, both cofinanced with FEDER. M.D. Ruiz-Rodríguez was recipient of a predoctoral fellowship associated to AGL2008-02305.Peer reviewe